Chevron Left
返回到 Mathematics for Machine Learning: PCA

學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

4.0
2,274 個評分
569 條評論

課程概述

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

熱門審閱

JS
2018年7月16日

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

NS
2020年6月18日

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

篩選依據:

451 - Mathematics for Machine Learning: PCA 的 475 個評論(共 565 個)

創建者 Rafael C

2019年12月7日

definitely one of the most catastrophic courses I've ever taken on Coursera...

創建者 Meraldo A

2018年5月8日

The course content was good; however, it was not well explained at times.

創建者 connie

2020年3月21日

I think content of first 2 weeks are disconnect with 3rd and 4th weeks

創建者 Alexander

2019年11月6日

Math for the sake of math. Too big jumps in calculations, too complex.

創建者 k v k

2018年11月30日

its a good course to learn mathematics essential for machine learning

創建者 Rafael C

2019年9月24日

The Classes didn't give the knowledge to solve the assignments.

創建者 Shuyu Z

2019年10月18日

The videos and instructions for the assignment are not clear.

創建者 gaurav k

2019年7月3日

More examples and visualization should be there to explain.

創建者 Malcolm M

2019年3月5日

Far more challenging than the first two courses.

創建者 A. S M S H

2020年6月2日

Theories should be explained more detailed.

創建者 Reinaldo L N

2020年2月26日

Last assignment was hell on Earth...

創建者 kirellos h

2020年4月8日

This course needs more examples.

創建者 Sean W

2019年11月25日

Notebook extremely buggy

創建者 Felipe M

2020年7月26日

It is a shame that this course isn't taught in a favorable way, as the content it has is very interesting and valuable. I found that the instructor lacked the enthusiasm that David Dye and Sam Cooper had in the previous courses, which obviously doesn't change the content of the course but definitely makes the learning experience worse. The lectures were also quite fast-paced and not very clear, I feel that this course should have been longer as when it was time to do the graded assignments, I had very little intuitive understanding of the concepts learned. The programming assignments were also the worse of the three courses; this is a combination of what I believe to be an issue with Coursera's online programming environment and the assignments themselves. The assignments were poorly explained and usually involved skills that were not even presented in lectures, which meant that unfortunately I had to rely heavily on books from the internet and assistance from fellow peers in the forums. Apart from requiring skills that were not taught, the Jupyter Notebook was unorganized in the sense that I felt unclear about where I should edit, where I should not. The programming assignments with the previous courses in this specialization were done in a much better way, guiding us to the solution while still demanding creativity and insight into the concepts, while the ones in PCA were messy. This is really sad as this is the most programming-heavy course. Overall I am quite disappointed with this course, it is a frustrating way to end this specialization with the two amazing previous courses.

創建者 Pedro L

2020年4月25日

Having taken the other two courses for this specialization, a certain standard was defined and expected. The other two courses had solid basis explained by the professors, and the assignments reflected well from the lessons showing a lineal progression to adequate difficulty.

In this course unfortunately it is not the case, the maths and basis are explained well enough, with extra lectures and side investigations needed from the user side in order to fully understand each lecture, and then the assignments. Don't expect immediate response form mentors nor teaching staff, and neither a well thought difficulty progression. The assignments done by hand and examples taught during lectures DO NOT reflect the difficulty level on programming assignments because it is expected you already have previous experience with python (which is rather frustrating as I took this course expecting to be entry level only on this language).

TL;DR: Take the first two courses if you wanna strengthen your basis, but the last course is not recommended

創建者 Abhishek J

2020年7月30日

Poor programming assignments, lots of error. Also, the teaching staff has to pull their socks up. No intuition behind anything, only throwing formulas one after the other. I must say if this is the stuff Coursera has to offer then it's not far that other online platforms will take over. No offense but I sincerely request the instructor to improve his teaching skills, as this kind will take him nowhere. It might sound harsh but it's the reality. Nevertheless, I learned something new which will hopefully help in my future, and for that, I will like to thanks the whole teaching staff. I hope you all continue this great initiative, provide quality content, and make learning as easy and affordable as possible. I Will be looking forward to more courses from your side but this time, please come up with new and exciting ways to explain mathematical stuff. Once again Kudos to the teachers and all the students who completed the course!!

創建者 Erik P

2020年2月12日

The first two courses in this series are excellent. However, this third course is taught by a new teacher and this introduces a remarkable drop in quality.

There are of cause different styles of teaching. However, as a minimum a teacher should strive towards conveing to students the importance of the subject at hand and the intuition behind it. However, this teacher settles for monotonously writing out formulas and definitions that can simply be read in the course formula PDF. Thus, watching the videos becomes a waste of time. In turn, this makes it harder to complete quizzes and assignments since one first has to go searching the internet for web pages that actually explain rather than simply state formulas that one needs to combine and apply in order to solve the assignments.

創建者 Nicholas T

2020年8月31日

I found this course to be rather lacking in what it lists as pre-requisites. I found the need to take a course on numpy while I took this course. Also, I'm just confused as to why this is part 3 of the specialization. Why not do a section on probability/stats to prepare for machine learning? I like all the professors, but there's only so much you're going to learn. I found I needed to constantly use the resources, and they are good, but the resources were better than the assignments and instruction, so... I would suggest saving your money.

創建者 noel s

2020年7月22日

The intermediate level of this course is accurate, but mainly because of the course's structure. In my opinion this course should not be a part of the specialization as the PCA is already covered in the first two courses. Although this third class is more (and almost only) about the maths I found it confusing in relation with the previous course and their explanation of PCA. Programming assignments are difficult and help the student to think by itself, however they are buggy which may take away the struggling student motivation.

創建者 Sagar L

2020年3月21日

Although the topics and lecturer's delivery were nice, but as compared to the two previous courses of the specialization, this one doesn't fare well. The content in the video lessons and that in the notebook were not really planned well in terms of scope. A participant who isn't already familiar with these concepts, would struggle a lot. Only if the reading material, video content and notebook assignments were designed keeping that in mind, it would have been better. Apart from that it was a good course.

創建者 Vitali Z

2020年8月22日

Slow notebooks, bad explanations, unclear what to do in the notebooks.

I don't know why i spent so much time to finish the course- maybe because of my perfectionism didn't let me stop trying.

I guess the matter itself is good, but:

1. you probably got to re-record all the videos a little more bit by bit with more examples

2. fix the slow notebooks

3. more assertions for each function instead of for the whole thing in the notebooks

4. more detailed explanations what we are even doing there

創建者 Tobias T

2019年7月14日

If you like traditional lectures, which you go into a math class then feel puzzled, then go for it. Otherwise, the contents of this course are simply going through the mathematics equations and definitions, which can easily be found in textbooks. Ironically, the previous two courses in this specialization used lots of graphics and animations to help you understand the maths (either in terms of equation-wise or intuitively), this course completely lacks this element.

創建者 Mark C

2018年7月30日

Only on week 1 but this is already a disappointment compared to the first two classes in the Math for ML series which were excellent. Some content is presented too fast. Quiz questions are ambiguous. I already paid for the class so I will finish the content but not worry about passing quizzes and assignments. Had I known it would be like this I wouldn't have paid for it. Check out the other reviews and forum discussions to see what others think.

創建者 Max B

2019年8月14日

Pretty bad all around.

The teacher keeps throwing formulas without taking the time to explain why they are useful, and what they represent.

The first two courses were really good, and this one is a bummer.

Most of what I learned was learned elsewhere, the course acted as a detailed syllabus with some practice quiz (of relatively poor quality).

It's still worth taking if you completed the first two courses and want the specialization certification.

創建者 Nouran G

2018年10月11日

Course is inconsiderate to new learners in that new concepts were very sloppily introduced. Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best. Unlike the other two courses, it assumes python knowledge, doesn't explain relevant syntax in the assignments; which made me take a lot of long unnecessary detours to get the python implementation right.